Predicting the geographic distribution of a species from presence-only data subject to detection errors




Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point-process models and binary-regression models for case-augmented surveys provide consistent estimators of a species’ geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point-process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence-only sample sizes. Analyses of presence-only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site-occupancy analyses of detections and nondetections of these species.
Publication type Article
Publication Subtype Journal Article
Title Predicting the geographic distribution of a species from presence-only data subject to detection errors
Series title Biometrics
DOI 10.1111/j.1541-0420.2012.01779.x
Volume 68
Issue 4
Year Published 2012
Language English
Publisher The International Biometric Society
Contributing office(s) Southeast Ecological Science Center
Description 10 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Biometrics
First page 1303
Last page 1312
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